Modern organizations gather large amounts of data to get valuable insights to improve their decision-making and lead to business growth. However, as you might know, data isn’t always perfect. It can contain old information, have gaps, or be inaccurate.
Companies that want to make the most out of their data have to ensure their data has value. That is where data enrichment comes into play. Data enrichment transforms your data into extensive profiles to get in-depth insights and support analytics.
However, not all data enrichment is the same. Most of it focuses on customer enrichment, which can help refine different aspects of customer data.
Data enrichment types
You can enrich various customer data, but the following are crucial for gaining valuable insights.
Geographic
Geographic data enrichment includes adding location-relevant data to existing datasets with customer addresses. It can consist of longitude, latitude, postal data, or different location information complementing existing datasets.
You may already know this process as customer enrichment, meaning that the main focus is on customers, or more specifically, on their locations. Companies can acquire this data in ZIP codes, mapping insights, boundaries between cities, and other forms.
Adding information can help in several ways. For example, retailers can use enriched geographic data to find the best location for a new store.
Demographic
Demographic data enrichment is all about getting added demographic information about customers such as income level, marital status, and level of education. You can gather demographic data from many different sources.
A dataset can include different car types, property values, or anything else, but what’s essential for demographic enrichment is the goal. For example, if you’re looking to offer credit cards to a specific group of customers, you can acquire a database with your customers’ credit scores.
With this enrichment, you will be able to target potential customers in a better way and create personalized marketing strategies.
Behavioral
Enriching behavioral data means adding insights and patterns that show how your customer profiles behave. The more behavioral patterns you add to a specific user profile, the easier it will be to determine that customer’s interests, journey, and what impacts their buying decisions.
This data enrichment can help improve lead generation, increase conversions, retain more customers, create a better user experience, improve marketing strategies, etc.
Contact enrichment
Contact enrichment or lead data enrichment involves adding information related to contacts, including their phone numbers, job positions, company descriptions, business emails, etc.
With this kind of data, companies can nurture meaningful relationships and have an extensive database of leads and customers that they can engage at the right time.
Essential characteristics of data enrichment
Data enrichment can benefit your company in many ways. Here are the most notable perks you will enjoy by enhancing your incomplete or missing data.
Improved data quality
Data enrichment is an ongoing process. Companies do it continuously to meet the constantly changing demands of their customers. Organizations often set up this process through data scraping from multiple sources.
However, it’s essential to ensure that the gathered data is complete and accurate. Inadequate practices could lead to wrong conclusions and poor business decisions. That’s why companies need to take the proper steps toward ensuring data quality.
You must profile data to identify any potential irregularities when accessing data sources. The whole process needs to have defined metrics for standardization and data cleansing. Ongoing monitoring of data quality and data verification is also crucial for ensuring that the quality is up to standards.
Data monitoring and updating
No matter what kind of entity they describe, all data types change over time. Updating data is vital to prevent it from hampering business decisions. For example, if an online store uses outdated customer data to create a new sales campaign, it will probably deliver irrelevant offers.
People’s purchasing habits change often, and it’s necessary to know the current situation. Just take a look at how the COVID-19 pandemic changed things. Validating and cleaning data periodically to ensure continuous quality, consistency, and relevance is vital for long-term results.
Data segmentation
Grouping data into different categories is always a good practice, so organizations need to narrow their data sets for better target segmentation. Data segmentation makes data enrichment methods more effective.
However, before segmenting your data, you must determine your business goals and target market. With this information, you can evaluate those processes that you can improve with enhanced data.
Setting up an effective ELT pipeline
Before combining data with your existing database, you first have to clean it and check its quality. You can use the Extract, Transform, and Load process, also known as ETL, to combine data with your database.
Before extraction, it’s crucial to assess all the data in your repositories. It’s also critical to check if you must correct the current data and if you need more information.
Companies often have large volumes of data, but not all of it is relevant for their efforts. That’s why ETL is necessary to ensure usable data from data enrichment.
Bottom line
An adequate data enrichment strategy is essential for any modern business organization. Keeping your data relevant and up-to-date allows you to target consumers more accurately and make informed business decisions.
Fine-tuning the whole process requires time, but it pays off in the long run since it improves all aspects of your business.
